The signature method is a generic way of extracting feature sets for sequential data. The basic idea is to embed the data as a path \([0,1] \rightarrow \mathbb {R}^d\) , and then to use features from its signature, which is the graded sequence of its iterated integrals. The coordinates of the signature are definite integrals of the path \((x_t,y_t,\dots )_{0\le t \le 1}\)  such as \(\int _{t}dx_t\) , \(\int _{t}dy_t\) , \(\int _{t<s}dx_tdx_s\) , \(\int _{t<s}dx_tdy_s\) , and so on. This method has achieved success in several recent machine learning applications to financial data, clinical symptoms, handwriting recognition, and more [1]}, [2]}. The notion of path signature originates in the fundamental theory of rough paths [3]}, [4]}.
